Method to enable vehicles to avoid weigh stations

ABSTRACT

Method for controlling weighing of a vehicle travelling on a road by means of a weigh station alongside the road includes determining weight of the vehicle using a processor on-board the vehicle and transmitting from the vehicle using a telematics device, the determined weight of the vehicle to the weigh station. Determining the weight of the vehicle may entail processing inertial property data from an inertial measurement unit (IMU) into an indication of the weight of the vehicle. The inertial property data from the IMU may be multiple sets of inertial property data obtained over time. The IMU may be calibrated, using the processor, based differential motion of the vehicle over a period of time as determined by a location positioning system. A Kalman filter may also be used. A vehicle transmitting its determined weight avoids stopping at the weigh station.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.13/649,807 filed Oct. 11, 2012, which is a divisional application ofU.S. patent application Ser. No. 12/028,956 filed Feb. 11, 2008, nowabandoned, which is:

1. a CIP of U.S. patent application Ser. No. 11/082,739 filed Mar. 17,2005, now U.S. Pat. No. 7,421,321, which is a CIP of U.S. patentapplication Ser. No. 10/701,361 filed Nov. 4, 2003, now U.S. Pat. No.6,988,026, which is a CIP of U.S. patent application Ser. No. 10/638,743filed Aug. 11, 2003, now U.S. Pat. No. 7,284,769;

2. a CIP of U.S. patent application Ser. No. 11/131,623 filed May 18,2005, now U.S. Pat. No. 7,481,453, which is a CIP of U.S. patentapplication Ser. No. 10/638,743 filed Aug. 11, 2003, now U.S. Pat. No.7,284,769;

3. a CIP of U.S. patent application Ser. No. 11/833,033 filed Aug. 2,2007, now abandoned, which is a CIP of U.S. patent application Ser. No.10/638,743 filed Aug. 11, 2003, now U.S. Pat. No. 7,284,769; and

4. a CIP of U.S. patent application Ser. No. 11/833,052 filed Aug. 2,2007, now U.S. Pat. No. 8,060,282, which is a CIP of U.S. patentapplication Ser. No. 10/638,743 filed Aug. 11, 2003, now U.S. Pat. No.7,284,769.

All of the above applications and patents, and any applications,publications and patents mentioned below, are incorporated by referenceherein in their entirety and made a part hereof.

FIELD OF THE INVENTION

The present invention relates to methods to enable vehicles to avoidstopping at weigh stations to be weighed and ensuring safe travel of avehicle or safety of an occupant of the vehicle.

BACKGROUND OF THE INVENTION

In the United States, taxes are often collected from trucking companiesand truck owners based in part on weight of the truck. To determinetaxes owed, weight/inspection stations are utilized on most highways,which are also useful to identify trucks exceeding legal weight limitsfor the road on which the weigh station is alongside. Signs directtrucks to pull into the stations to have their weight checked to ensurethat the trucks are in compliance with federal and state weightregulations. These stations use static scales, which require that thetruck pull onto the scale and stop while being weighed.

The weighing process at a weigh station can take a few minutes or longerduring peak truck travel times. This delay can represent a significantcost to trucking operations, particularly in situations where“just-in-time” shipping is being utilized and delays can result inreduced revenues.

An example of a prior art electronic screening system is “PrePass”(www.prepass.com). As described in U.S. Pat. No. 6,980,093, PrePass is asystem that allows participating transponder-equipped commercialvehicles to bypass designated weigh stations and other such facilities.A vehicle participating in the PrePass system is identified in adatabase proprietary to the PrePass system, as part of thepre-certification process conducted when the vehicle is registered inthe system. The database contains weight information and “credentialinformation” regarding the vehicle and correlates this information witha PrePass transponder ID number that corresponds to a transpondercarried in the vehicle. As a vehicle approaches a PrePass-equippedweight/inspection station, it comes into the range of an AutomaticVehicle Identification (AVI) antenna, which communicates with thetransponder to identify the transponder ID number, thereby giving thePrePass system access to the saved data for that vehicle. At the sametime, the vehicle passes over a WIM scale, and the weight data obtainedfrom the scale is also transmitted back to the PrePass system. Thisallows the PrePass system to verify that the vehicle should be able tobypass the inspection station. Assuming everything is verified, a signalis sent to the transponder causing it to issue an audible signal and“go” indication (e.g., a “green light”) directing the driver to pass thestation without needing to stop.

As also described in the '093 patent, an e-screening system concept thatcomplies with the architecture of the “CVISN” architecture prescribed bythe Federal Motor Carrier Safety Administration is described in“Introductory Guide to CVISN”, section 2.7 The CVISN e-screening concepthas many advantages because of its use of a standardized nationaldatabase that is shared among the states with data and methods ofexchange that are standardized according to CVISN architecture. Whilehaving many advantages when compared to PrePass, both of these sufferfrom some disadvantages. Mainline screening alone, based upon AVI, islargely ineffective because it cannot reach the vast majority of trucksthat do not operate with a transponder. Mainline screening systems mustsend all the vehicles that do not have transponders into the weighstation. At many stations, queuing backups would not be alleviated untilat least 30-50% of the mainline vehicles were bypassed. Currently, onthe order of 1-2% of commercial vehicles carry transponders, soe-screening systems designed around mainline screening alone cannot beeffective. Additionally, mainline (i.e., highway-based) WIM scales areinherently inaccurate because the trucks are operating at highway speedswhen being weighed using the mainline WIM scale. Vehicle dynamicsgenerated by bumps in the highway road surface and the path of thevehicle contribute to inaccuracies when using mainline WIM scales. As aresult, even the transponder-equipped vehicles tend to be directed intothe weigh/inspection station to be subjected to the more rigorous andtime-consuming static weighing system and detailed inspection process,only to be found in compliance and redirected back to the highway aftersignificant (and unnecessary) delay.

The '093 asserts that it is desirable to have an e-screening system thatcan conduct a secondary screening process, based upon AVI andalternative vehicle-identification technology after an initial (primary)screening process to reduce the number of vehicles that are subjected tothe time-consuming static-scale weighing process improperly due to theinaccuracies inherent in mainline WIM scale measurements.

To this end, the '093 patent describes a system for electronicallyscreening vehicles traveling on a road having an exit ramp along whichis situated a vehicle weigh station, the vehicle weigh station having astatic scale configured to make static weight measurements of thevehicles. The system includes a first weigh-in-motion (WIM) scale,positioned along the road in the proximity of the vehicle weigh station,configured to make a first weight measurement of the vehicles; a secondWIM scale, positioned along the exit ramp and associated with the weighstation, configured to make a second weight measurement of the vehicles;one or more indicator signals, positioned near the first and second WIMscales so as to be perceivable by drivers of the vehicles, which, whenactivated, direct the traveling vehicles to either pull onto or bypassthe exit ramp and/or the static scale; and a processor, coupled to thefirst and second WIM scales, the static scales and the one or moreindicator signals. The processor is configured to (i) correct the firstand second WIM weight measurements based on the static weightmeasurements; and (ii) activate the one or more indicator signals basedon the corrected first and second weight measurements.

SUMMARY OF THE INVENTION

A method for controlling weighing of a vehicle travelling on a road bymeans of a weigh station alongside the road in accordance with theinvention includes determining weight of the vehicle using a processoron-board the vehicle and transmitting from the vehicle using atelematics device, the determined weight of the vehicle to the weighstation. Determining the weight of the vehicle may entail processinginertial property data from an inertial measurement unit (IMU) into anindication of the weight of the vehicle. The inertial property data fromthe IMU may be multiple sets of inertial property data obtained overtime. The IMU may be calibrated, using the processor, based differentialmotion of the vehicle over a period of time as determined by a locationpositioning system. A Kalman filter may also be used. A vehicletransmitting its determined weight avoids stopping at the weigh station.

Another method for controlling travel of vehicles on a road having aweigh station alongside the road at which vehicles must provide theirvehicle weight at least for some designated times and designated vehicletypes in accordance with the invention includes for those vehiclesequipped with an on-board weight determining unit, determining weight ofthe vehicle using a processor on-board the vehicle and transmitting fromthe vehicle using a telematics device, the determined weight of thevehicle to the weigh station. A vehicle transmitting its determinedweight avoids stopping at the weigh station. The on-board weightdetermining unit includes an inertial measurement unit (IMU) and theweight of the vehicle may then be determined by processing inertialproperty data from the IMU into an indication of the weight of thevehicle. The inertial property data from the IMU may be multiple sets ofinertial property data obtained over time. The IMU may be calibrated,using the processor, based differential motion of the vehicle over aperiod of time as determined by a location positioning system. A Kalmanfilter may also be used.

A method for managing road information in accordance with the inventionincludes obtaining road properties from vehicles during travel on thevehicle on a road at an off-vehicle location, the road properties beingobtained using an inertial measurement unit on board each vehicle andtransmitted from the vehicle using a telematics device, storing the roadproperties from the vehicles at the off-vehicle locations in a datastorage device, and selectively distributing from the data storagedevice to the telematics device on vehicles traveling on the road, atleast part of the collected road properties to the vehicles traveling onthe road. In one embodiment, the road properties are associated with theweather in the area of the road at the off-vehicle location, in whichcase, the collected road properties is distributed to the vehiclestraveling on the road based on the weather.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings are illustrative of embodiments of the inventionand are not meant to limit the scope of the invention as encompassed bythe claims.

FIG. 1 is a view of the front of the passenger compartment of a motorvehicle, with portions cut away and removed, having dual airbags and asingle point crash sensor and crash severity forecaster including anaccelerometer and using a pattern recognition technique.

FIG. 1A is an enlarged view of the sensor and diagnostic module shown inFIG. 1.

FIG. 2 is a diagram of a neural network used for a crash sensor andcrash severity forecaster designed based on the teachings of inventionand having more than one output node.

FIG. 3 is a schematic illustration of a generalized component withseveral signals being emitted and transmitted along a variety of paths,sensed by a variety of sensors and analyzed by the diagnostic module inaccordance with the invention and for use in a method in accordance withthe invention.

FIG. 4 is a schematic of a vehicle with several components and severalsensors and a total vehicle diagnostic system in accordance with theinvention utilizing a diagnostic module in accordance with the inventionand which may be used in a method in accordance with the invention.

FIG. 5 is a flow diagram of information flowing from various sensorsonto the vehicle data bus and thereby into the diagnostic module inaccordance with the invention with outputs to a display for notifyingthe driver, and to the vehicle cellular phone for notifying anotherperson, of a potential component failure.

FIG. 6 is a flow chart of the methods for automatically monitoring avehicular component in accordance with the invention.

FIG. 7 is a schematic illustration of the components used in the methodsfor automatically monitoring a vehicular component.

FIG. 8 is a schematic of a vehicle with several accelerometers and/orgyroscopes at preferred locations in the vehicle.

FIG. 9 is a block diagram of an inertial measurement unit calibratedwith a GPS and/or DGPS system using a Kalman filter.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 1. Crash Sensors

1.1 Pattern Recognition Approach to Crash Sensing

Throughout much of the discussion herein, the neural network will beused as an example of a pattern recognition technique or algorithm sincethe neural network is one of the most developed of such techniques.However, it has limitations that are now being addressed with thedevelopment of newer pattern recognition techniques as well as betterneural network techniques such as combination or modular neuralnetworks. These limitations involve the difficulty in describing theprocess used in classifying patterns with the result that there is afear that a pattern that was not part of the training set might bemissed. Also, the training process of the neural network does notguarantee that convergence to the best solution will result. One suchexample is the local minimum problem wherein the training algorithmconverges on a result that is not the best overall or global solution.These problems are being solved with the development of newer patternrecognition techniques such as disclosed in various U.S. patents andtechnical papers. One invention disclosed herein is the use of patternrecognition techniques including neural networks, regardless of theparticular technique, to provide a superior smart airbag system. Inparticular, genetic algorithms are being applied to aid in selecting thebest of many possible choices for the neural network architecture. Theuse of genetic algorithms helps avoid the local minimum situation sinceseveral different architectures are tried and the best retained.

The pattern recognition algorithm, which forms an integral part of thecrash sensor described herein, can be implemented either as an algorithmusing a conventional microprocessor, FPGA or ASIC or through a neuralcomputer. In the first case, the training is accomplished using a neuralpattern recognition program and the result is a computer algorithmfrequently written in the C computer language, although many othercomputer languages such as FORTRAN, assembly, Basic, etc. could be used.In the last case, the same neural computer can be used for the trainingas used on the vehicle. Neural network software for use on aconventional microcomputer is available from several sources such asInternational Scientific Research, Panama City, Panama. An example of aneural network-based crash sensor algorithm produced by ISR softwareafter being trained on a crash library created by using data supplied byan automobile manufacturer for a particular model vehicle plusadditional data created by using the techniques of crash and velocityscaling is:

* Neural net for crash sensor. 23 August 94. 50 input nodes, * 6 hiddennodes (sigmoid transfer function), 1 output node (value 0 or 1). *Network was trained using back propagation with Logicon Projection. *Yin(1-50) are raw input values. Xin(1-50) are scaled input values. *Yin(50) is the sum of the latest 25 accelerations, in tenths of a g, *Yin(49) is the sum of the previous 25, etc. The time step is 80microsecond. logical function nnmtlpn3( Yin, firesum, Yout )  real*4firesum, Yin(50), Yout   integer i, j  real*4 biashid(6), biasout,fire_criterion, hiddenout(6), NormV, NV(4),  & offset_in(50),offset_out, scale_in(50), scale_out, wgthid(51,6),  & wgtout(6),Xin(51), Xsum   parameter( fire_criterion = 0.0 )  data scale_in/(omitted) /   data offset_in/ (omitted) /  data scale_out, offset_out /0.625, 0.5 /   data NV/ 2.0, 7.0, 7.0711002, 50.000458 /  data biashid/-49.110764, -69.856407, -48.670643,  & -48.36599, -52.745285, -49.013027/  data biasout/ 0.99345559 /  data wgthid/ (omitted) /   data wgtout/(omitted) /  NormV = 0.0  do i=1,50  Xin(i) = scale_in(i) * Yin(i) -offset_in(i)  NormV = NormV + Xin(i) * Xin(i)  enddo   NormV = NV(1) *NV(2) * NV(3) / ( NV(4) + NormV )   do i=1,50  Xin(i) = NormV * Xin(i) enddo   Xin(51) = NV(2) - NV(3) * NormV  do i=1,6  Xsum = biashid(i) do j=1,51   Xsum = Xsum + wgthid(j,i) * Xin(j)  enddo  hiddenout(i) =1.0 / ( 1.0 + exp( -Xsum ) )  enddo  firesum = biasout  do i=1,6 firesum = firesum + wgtout(i) * hiddenout(i)  enddo   Yout =offset_out + scale_out * tanh(firesum)   if( firesum .GE. fire_criterion) then  nnmtlpn3 = .TRUE.  else  nnmtlpn3 = .FALSE.  endif  return end

Neural computers on a chip are now available from various chipsuppliers. These chips make use of massively parallel architecture andallow all of the input data to be processed simultaneously. The resultis that the computation time required for a pattern to be tested changesfrom the order of milliseconds for the case of themicroprocessor-implemented system to the order of tens to hundreds ofmicroseconds for the neural computer. With this computational speed, oneneural computer can easily be used for several pattern recognitionimplementations simultaneously even during the crash event includingdynamic out-of-position and crash sensing. A discussion of the structureof such a neural computer can be found on page 382 of Digital NeuralNetworks, by Kung, S. Y., PTR Prentice Hall, Englewood Cliffs, N.J.,1993.

As an example of an algorithm produced by such software after beingtrained on a crash library created by using data supplied by anautomobile manufacturer for a particular model vehicle plus additionaldata created by using the techniques of crash and velocity scaling, thenetwork was trained to give a value of 1 for triggering the airbag and 0for not triggering. In the instant case, this value would depend on thetype of gas control module that is used and in general would varycontinuously from 0 to 1 with the particular value indicative of theaction to be taken by the gas control module, such as adding more gas tothe airbag.

Examples of neural networks in several forms will be discussed in moredetail below in several sections of this application.

1.2 Electronic Crash Sensors

An airbag electronic sensor and diagnostic module (SDM) is typicallymounted at a convenient location in the passenger compartment such asthe transmission tunnel or firewall. FIG. 1 is a view of the front of apassenger compartment 50 of an automobile with portions cut away andremoved, having dual airbags 51, 52 and an SDM 55 containing a non crushzone electronic crash sensor and crash forecasting algorithm,(hereinafter this combination will be referred to as a crash sensor)comprising one to three accelerometers and zero to three gyroscopes 56,one or more analog to digital converters (ADC) 57 and a patternrecognition algorithm contained within a microprocessor 59, all of whichmay be mounted on a single circuit board and electrically coupled to oneanother (see FIG. 1A). Alternately, the microprocessor 59 can be aneural computer.

A tri-axial accelerometer is a device that includes three accelerometersand measures accelerations in three orthogonal directions that aretypically the longitudinal, lateral and vertical directions, althoughthere are sometimes reasons to use a different orientation. Such adifferent orientation can be useful to remove some of the bias errors inthe accelerometers by, for example, allowing each accelerometer to bepartially influenced by gravity. Also, in some applications, thetri-axial accelerometer is intentionally rotated relative to the vehicleto expose different accelerometers to gravity again for accuracycalibration purposes. An alternate method is to electronically test theacceleration sensing elements by exposing them to an electric field andmeasure their response. Such an accelerometer is called a “testable”accelerometer.

The circuit board of the SDM 55 also optionally contains a capacitor 61as a backup power supply, other electronic components 58 and variouscircuitry. The SDM is connected to the airbags 51, 52 with wires 53 and54 (shown in dotted lines in FIG. 1), although a wireless electricalconnection is also a possibility as wireless data transfer has becomemore reliable. In this embodiment, the pattern recognition techniqueused is a neural network that analyzes data from one, two or threeaccelerometers, and optionally up to three gyroscopes, to determinewhether the vehicle is experiencing a crash from any direction.Alternately, an IMU may be used. If the neural network determines, e.g.,by analysis of a pattern in the signals emanating from theaccelerometer(s) 56 and gyroscope(s) 56, that the accident meritsdeployment of one or more protection or restraint systems, such as aseatbelt retractor, frontal or side airbag, or a movable headrest, itinitiates such deployment and thus constitutes in this regard airbagdeployment initiation means. It also may determine the settings for anairbag inflation/deflation control module which determines how much gasis to be generated, how fast it is to be generated, how much should befed into the airbag, how much should be dumped to the atmosphere and/orhow much should be permitted to exhaust from the airbag. The particularmethod and apparatus for controlling the flows of gas into and/or out ofthe airbag will depend on the particular system design. The controllerfor any such system will hereinafter be referred to as the gas controlmodule and is illustrated in FIG. 1A schematically as 60.

For frontal impacts, for example, a signal is sent through wires 53 and54 to initiate deployment of airbags 51 and 52 and to control the gasflow into and/or out of each airbag 51, 52 through the gas controlmodules (not shown) for each airbag. The ADC 57 is connected to theacceleration sensor, in this case the tri-axial accelerometer 56, andconverts an analog signal generated by one or more of the accelerometers56 representative of the acceleration thereof, and thus the vehicle,into a digital signal. In one embodiment, the ADC 57 may derive thedigital signal from the integral of the analog signal. Naturally, manyof the components of the printed circuit board can be incorporated intoan ASIC as is obvious to those skilled in the art.

The tri-axial accelerometer and/or gyroscopes 56 (or IMU) are mounted bysuitable mounting structure to the vehicle and can be mounted in avariety of positions to sense, e.g., frontal impacts, side impacts, rearimpacts and/or rollovers. In another embodiment described below, themicroprocessor 59 may include a detection system for detecting when theoccupant to be protected by the deployable airbags 51, 52 isout-of-position and thereupon to suppress deployment thereof. Also, thedetection system may be applied to detect the presence of a rear-facingchild seat positioned on a passenger seat and thereupon to suppressdeployment of the airbag. In each case, the microprocessor or neuralcomputer 59 performs an analysis on signals received from appropriatesensors and corresponding ADCs. Recent advances in computational theorysuggest that a form of computation using analog data rather than digitaldata may become viable. One example is the use of optical correlatorsfor object detection and identification in the military where theoptical signal from a video scene is converted to its Fourier transformusing diffraction techniques.

The pattern recognition crash sensor described and illustrated in FIGS.1 and 1A is capable of using information from three accelerometers 56,for example, each measuring acceleration from an orthogonal direction.As will be described in more detail below, other information can also beconsidered by the pattern recognition algorithm such as the position ofthe occupants, noise, data from anticipatory acoustic, radar, infraredor other electromagnetic sensors, seat position sensors, seatbeltsensors, speed sensors, gyroscopes or any other information present inthe vehicle which is relevant. Since the pattern recognition algorithmis trained on data from real crashes and non-crash events, it can handledata from many different information sources and sort out what patternscorrespond to airbag-required events in a way that is nearly impossiblefor an engineer to do. For this reason, a crash sensor based on neuralnetworks, for example, will invariably perform better than one devisedby engineers. The theory of neural networks including many examples canbe found in several books on the subject including: Techniques andApplication of Neural Networks, edited by Taylor, M. and Lisboa, P.,Ellis Horwood, West Sussex, England, 1993; Naturally IntelligentSystems, by Caudill, M. and Butler, C., MIT Press, Cambridge Mass.,1990; J. M. Zaruda, Introduction to Artificial Neural Systems, WestPublishing Co., N.Y., 1992 and, Digital Neural Networks, by Kung, S. Y.,PTR Prentice Hall, Englewood Cliffs, N.J., 1993, Eberhart, R., Simpson,P. and Dobbins, R., Computational Intelligence PC Tools, Academic Press,Inc., 1996, Orlando, Fla. The neural network pattern recognitiontechnology is one of the most developed of pattern recognitiontechnologies. Newer and more efficient systems are now being developedsuch as the neural network system which is being developed by Motorolaand is described in U.S. Pat. No. 5,390,136 and U.S. Pat. No. 5,517,667.The neural network will be used here to illustrate one example of apattern recognition technology but it is emphasized that this inventionis not limited to neural networks. Rather, the invention may apply anyknown pattern recognition technology. A brief description of the neuralnetwork pattern recognition technology is set forth below.

A diagram of one example of a neural network used for a crash sensordesigned based on the teachings of this invention is shown in FIG. 2.The process can be programmed to begin when an event occurs whichindicates an abnormal situation such as the acceleration in thelongitudinal direction, for example, exceeding the acceleration ofgravity, or it can take place continuously depending on the demands onthe computer system. The digital acceleration values from the ADC 57 maybe pre-processed, for example by filtering, and then enteredsuccessively into nodes 1, 2, 3, . . . , N (this entry represented bythe arrows) and the neural network algorithm compares the pattern ofvalues on nodes 1 through N with patterns for which it has been trained.Each of the input nodes is connected to each of the second layer nodesh-1, . . . , h-n, called the hidden layer, either electrically as in thecase of a neural computer, to be described below, or throughmathematical functions containing multiplying coefficients calledweights, also described in more detail below. The weights are determinedduring the training phase while creating the neural network. At eachhidden layer node, a summation occurs of the values from each of theinput layer nodes, which have been operated on by functions containingthe weights, to create a node value. Similarly, the hidden layer nodesare connected to the output layer nodes O-1, O-2, . . . , O-n, which canbe only a single node representing the control parameter to be sent tothe gas control module, for example. If this value exceeds a certainthreshold, the gas control module initiates deployment of the airbag.

During the training phase, an output node value is assigned for everysetting of the gas control module corresponding to the desired gas flowfor that particular crash as it has occurred at a particular point intime. As the crash progresses and more acceleration values appear on theinput nodes, the value of the output node may change. In this manner, aslong as the crash is approximately represented in the training set, thegas flow can be varied at each one or two milliseconds depending on thesystem design to optimally match the quantity of gas in the airbag tothe crash as it is occurring. Similarly, if an occupant sensor and aweight sensor are present, that information can additionally be fed intoa set of input nodes so that the gas module can optimize the quantity ofgas in the airbag taking into account both the crash deceleration andalso the position, velocity, size and/or weight of the occupant tooptimally deploy the airbag to minimize airbag induced injuries andmaximize the protection to the occupant. Details of the manner in whicha neural network process operates and is trained are known and will notbe presented in detail here.

A time step, such as two milliseconds, is selected as the period inwhich the ADC pre-processes the output from the accelerometers and feedsdata to input node 1. Thus, using this time step, at time equal to 2milliseconds from the start of the process, node 1 contains a valueobtained from the ADC and the remaining input nodes have a random valueor a value of 0. At time equal 4 milliseconds, the value that was onnode 1 is transferred to node 2 (or the node numbering scheme isadvanced) and a new value from the ADC is fed into node 1. In a similarmanner, data continues to be fed from the ADC to node 1 and the data onnode 1 is transferred to node 2 whose previous value was transferred tonode 3 etc. The actual transfer of data to different memory locationsneed not take place but only a redefinition of the location that theneural network should find the data for node 1. For one preferredembodiment of this invention, a total of one hundred input nodes wereused representing two hundred milliseconds of acceleration data. At eachstep, the neural network is evaluated and if the value at the outputnode exceeds some value such as 0.5, then the airbags are deployed bythe remainder of the electronic circuit. In this manner, the system doesnot need to know when the crash begins, that is, there is no need for aseparate sensor to determine the start of the crash or of a particularalgorithm operating on the acceleration data to make that determination.

In the example above, one hundred input nodes were used, along withtwelve hidden layer nodes and one output layer node. Accelerations fromonly the longitudinal direction were considered. If other data such asaccelerations from the vertical or lateral directions or the output froma number of gyroscopes were also used, then the number of input layernodes would increase. If the neural network is to be used for sensingrear impacts, or side impacts, 2 or 3 output nodes might be used, onefor each gas control module, headrest control module etc. Alternately,combination, modular or even separate neural networks can be used. Thetheory for determining the complexity of a neural network for aparticular application is the subject of many technical papers and willnot be presented in detail here. Determining the requisite complexityfor the example presented herein can be accomplished by those skilled inthe art of neural network design and is discussed briefly below. Inanother implementation, the integral of the acceleration data is usedand it has been found that the number of input nodes can besignificantly reduced in this manner.

The neural network described above defines a method of sensing a crashand determining whether to begin inflating a deployable occupantprotection device, and at what rate, and comprises:

(a) obtaining one or more acceleration signals from one or moreaccelerometers mounted on a vehicle;

(b) converting the acceleration signal(s) into a digital time serieswhich may include pre-processing of the data;

(c) entering the digital time series data into the input nodes of aneural network;

(d) performing a mathematical operation on the data from each of theinput nodes and inputting the operated-on data into a second series ofnodes wherein the operation performed on each of the input node dataprior to inputting the operated on value to a second series node isdifferent from that operation performed on some other input node data;

(e) combining the operated-on data from all of the input nodes into eachsecond series node to form a value at each second series node;

(f) performing a mathematical operation on each of the values on thesecond series of nodes and inputting the operated-on data into an outputseries of nodes wherein the operation performed on each of the secondseries node data prior to inputting the operated on value to an outputseries node is different from that operation performed on some othersecond series node data;

(g) combining the operated on data from all of the second series nodesinto each output series node to form a value at each output series node;and,

(h) initiating gas flow into an airbag if the value on one output seriesnode is within a selected range signifying that a crash requiring thedeployment of an airbag is underway; and

(i) causing the amount of gas flow into or out of the airbag to dependon the value on that one output series node.

The particular neural network described and illustrated above contains asingle series of hidden layer nodes. In some network designs, more thanone hidden layer is used although only rarely will more than two suchlayers appear. There are of course many other variations of the neuralnetwork architecture illustrated above which appear in the literature.

The implementation of neural networks can have at least two forms, analgorithm programmed on a digital microprocessor or in a neuralcomputer. Neural computer chips are now available and neural computerscan be incorporated into ASIC designs. As more advanced patternrecognition techniques are developed, specially designed chips can beexpected to be developed for these techniques as well.

FIG. 3 provides the results of a neural network pattern recognitionalgorithm, as presented in U.S. Pat. No. 5,684,701, for use as a singlepoint crash sensor. The results are presented for a matrix of crashescreated according to velocity and crash scaling techniques. The tablecontains the results for different impact velocities (vertical column)and different crash durations (horizontal row). The results presentedfor each combination of impact velocity and crash duration consist ofthe displacement of an unrestrained occupant at the time that airbagdeployment is initiated and 30 milliseconds later. This is presentedhere as an example of the superb results obtained from the use of aneural network crash sensor that forms a basis of the instant invention.In FIG. 3, the success of the sensor in predicting that the velocitychange of the accident will exceed a threshold value is demonstrated. Inthe instant invention, this capability is extended to where theparticular severity of the accident is (indirectly) determined and thenused to set the flow of gas into and/or out of the airbag to optimizethe airbag system for the occupant and the crash severity.

Airbags have traditionally been designed based on the assumption that 30milliseconds of deployment time is available before the occupant, asrepresented by an unbelted dummy corresponding to the average male, hasmoved five inches. An occupant can be seriously injured or even killedby the deployment of the airbag if he or she is too close to the airbagwhen it deploys and in fact many people, particularly children and smalladults, have now been killed in this manner. It is known that this isparticularly serious when the occupant is leaning against the airbagwhen it deploys which corresponds to about 12 inches of motion for theaverage male occupant, and it is also known that he will be uninjured bythe deploying airbag when he has moved less than 5 inches when theairbag is completely deployed. These dimensions are based on the dummythat represents the average male, the so-called 50% male dummy, sittingin the mid-seating position.

The threshold for significant injury is thus somewhere in between thesetwo points and thus for the purposes of this table, two benchmarks havebeen selected as being approximations of the threshold of significantinjury. These benchmarks are, based on the motion of an unrestrainedoccupant, (i) if the occupant has already moved 5 inches at the timethat deployment is initiated, and (ii) if the occupant has moved 12inches by the time that the airbag is fully deployed. Both benchmarksreally mean that the occupant will be significantly interacting with theairbag as it is deploying. Other benchmarks could of course be used;however, it is believed that these two benchmarks are reasonable lackinga significant number of test results to demonstrate otherwise, at leastfor the 50% male dummy.

One additional feature, which results from the use of the neural networkcrash sensor of this invention, is that at the time the decision is madeto deploy the airbag and even for as long afterward as the sensor isallowed to run, in the above example, 200 milliseconds of crash data isstored in the network input nodes. This provides a sort of “black box”which can be used later to accurately determine the severity of thecrash as well as the position of the occupant at the time of the crash.If some intermediate occupant positions are desired, they could bestored on a separate non-volatile memory.

Above, the sensing of frontal impacts has been discussed using a neuralnetwork derived algorithm. A similar system can be derived for rear andside impacts especially if an anticipatory sensor is available as willbe discussed below. An IMU located at a single location in a vehicle cando an excellent job of monitoring the motions of the vehicle that couldlead to accidents including pre-crash braking, excessive yaw or pitchingor roll which could lead to a rollover event. If the vehicle also has aGPS system, then the differential motion of the vehicle over a period ofone second as measured by the GPS can be used to calibrate the IMUeliminating all significant errors. This is done using a Kalman filter.If a DGPS system is also available along with an accurate map, then thevehicle will also know its precise position within centimeters. Thishowever is not necessary for calibrating and thereby significantlyimproving the accuracy of the IMU and thus the vehicle motion can beknown approximately 100 times better than systems that do not use such aGPS-calibrated IMU. This greatly enhances the ability of vehicle systemsto avoid skidding, rollover and other out-of-control situations thatfrequently lead to accidents, injuries and death. This combination of aninexpensive perhaps MEMS-based IMU with GPS and a Kalman filter haspreviously not been applied to a vehicle for safety and vehicle controlpurposes although the concept has been used with a DGPS system for farmtractors for precision farming.

With an accurate IMU, as mentioned above, the weight of a variablyloaded vehicle can be determined and sent by telematics to a weighstation thereby eliminating the need for the vehicle to stop and beweighed. Referring to FIG. 9, the IMU 311 provides the inertialproperties to the processor 313 which determines the weight of thevehicle and uses a coupled telematics device 315 to provide thedetermined weight to the weigh station 316 situated alongside the roadon which the vehicle is travelling. For those vehicles equipped with theIMU 311, the processor 313 and the telematics device 315, and thus whichprovides its weight wirelessly to the weigh station as it approaches orpasses the weigh station, the vehicle does not have to stop at the weighstation.

Such an accurate IMU can also be used to determine the inertialproperties of a variably loaded vehicle such as a truck or trailer. Inthis case, the IMU output can be analyzed by appropriate equations of aneural network, and with assumed statistical road properties plusperhaps some calibration for a particular vehicle, to give the center ofmass of the vehicle as well as its load and moments of inertia. Withthis knowledge plus even a crude digital map, a driver can be forewarnedthat he might wish to slow down due to an upcoming curve. If telematicsare added, then the road properties can be automatically accumulated atan appropriate off-vehicle location and the nature of the road under allweather conditions can be made available to trucks traveling the road tominimize the chance of accidents. This information plus the output ofthe IMU can significantly reduce truck accidents. The information canalso be made available to passing automobiles to warn them of impendingpotential problems. Similarly, if a vehicle is not behavingappropriately based on the known road geometry, for example if thedriver is wandering off the road, traveling at an excessive speed forconditions or generally driving in an unsafe manner, the off-vehiclesite can be made aware of the fact and remedial action taken.

There are many ways to utilize one or more IMUs to improve vehiclesafety and in particular to prevent rollovers, out-of-control skidding,jack-knifing etc. In a simple implementation, a single IMU is placed atan appropriate location such as the roof of a truck or trailer and usedto monitor the motion over time of the truck or trailer. Based on theassumption that the road introduces certain statistically determinabledisturbances into the vehicle, such monitoring over time can give a goodidea of the mass of the vehicle, the load distribution and its momentsof inertia. It can also give some idea as to the coefficient of frictionon the tires against the roadway. If there is also one or more IMUslocated on the vehicle axle or other appropriate location that moveswith the wheels, then a driving function of disturbances to the vehiclecan also be known leading to a very accurate determination of theparameters listed above especially if both a front and rear axle are soequipped. This need not be prohibitively expensive as IMUs are expectedto break the $100 per unit level in the next few years.

As mentioned above, if accurate maps of information from other vehiclesare available, the IMUs on the axles may not be necessary as the drivingfunction would be available from such sources. Over the life of thevehicle, it would undoubtedly be driven empty and full to capacity sothat if an adaptive neural network is available, the system cangradually be trained to quickly determine the vehicle's inertialproperties when the load or load distribution is changed. It can also betrained to recognize some potentially dangerous situations such as loadsthat have become lost resulting in cargo that shifts during travel.

If GPS is not available, then a terrain map can also be used to providesome corrections to the IMU. By following the motion of the vehiclecompared with the known geometry of the road, a crude deviation can bedetermined and used to correct IMU errors. For example, if the beginningand end of a stretch of a road is known and compared with the integratedoutput of the IMU, then corrections to the IMU can be made.

The MEMS gyroscopes used in a typical IMU are usually vibrating tuningforks or similar objects. Another technology developed by the ScirasCompany of Anaheim, Calif., (The μSCIRAS multisensor, a CoriolisVibratory Gyro and Accelerometer IMU) makes use of a vibratingaccelerometer and shows promise of making a low cost gyroscope withimproved accuracy. A preferred IMU is described in U.S. Pat. No.4,711,125. One disclosed embodiment of a side impact crash sensor for avehicle in accordance with the invention comprises a housing, a masswithin the housing movable relative to the housing in response toaccelerations of the housing, and structure responsive to the motion ofthe mass upon acceleration of the housing in excess of a predeterminedthreshold value for controlling an occupant protection apparatus. Thehousing is mounted by an appropriate mechanism in such a position and adirection as to sense an impact into a side of the vehicle. The sensormay be an electronic sensor arranged to generate a signal representativeof the movement of the mass and optionally comprise a microprocessor andan algorithm for determining whether the movement over time of the massas processed by the algorithm results in a calculated value that is inexcess of the threshold value based on the signal. In the alternative,the mass may constitute part of an accelerometer, i.e., a micro-machinedacceleration sensing mass. The accelerometer could include apiezo-electric element for generating a signal representative of themovement of the mass.

An embodiment of a side impact airbag system for a vehicle in accordancewith an invention herein comprises an airbag housing defining aninterior space, one or more inflatable airbags arranged in the interiorspace of the system housing such that when inflating, the airbag(s)is/are expelled from the airbag housing into the passenger compartment(along the side of the passenger compartment), and an inflator mechanismfor inflating the airbag(s). The inflator mechanism may comprise aninflator housing containing propellant. The airbag system also includesa crash sensor for controlling inflation of the airbag(s) via theinflator mechanism upon a determination of a crash requiring inflationthereof, e.g., a crash into the side of the vehicle along which theairbag(s) is/are situated. The crash sensor may thus comprise a sensorhousing arranged within the airbag housing, external of the airbaghousing, proximate to the airbag housing and/or mounted on the airbaghousing, and a sensing mass arranged in the sensor housing to moverelative to the sensor housing in response to accelerations of thesensor housing resulting from, e.g., the crash into the side of thevehicle. Upon movement of the sensing mass in excess of a thresholdvalue, the crash sensor controls the inflator to inflate the airbag(s).The threshold value may be the maximum motion of the sensing massrequired to determine that a crash requiring deployment of the airbag(s)is taking place.

The crash sensor of this embodiment, or as a separate sensor of anotherembodiment, may be an electronic sensor and the movement of the sensingmass may be monitored. The electronic sensor generates a signalrepresentative of the movement of the sensing mass that may be monitoredand recorded over time. The electronic sensor may also include amicroprocessor and an algorithm for determining whether the movementover time of the sensing mass as processed by the algorithm results in acalculated value that is in excess of the threshold value based on thesignal.

In some embodiments, the crash sensor also includes an accelerometer,the sensing mass constituting part of the accelerometer. For example,the sensing mass may be a micro-machined acceleration sensing mass inwhich case, the electronic sensor includes a micro-processor fordetermining whether the movement of the sensing mass over time resultsin an algorithmic determined value which is in excess of the thresholdvalue based on the signal. In the alternative, the accelerometerincludes a piezo-electric element for generating a signal representativeof the movement of the sensing mass, in which case, the electronicsensor includes a micro-processor for determining whether the movementof the sensing mass over time results in an algorithmic determined valuewhich is in excess of the threshold value based on the signal.

1.3 Crash Severity Prediction

In the particular implementation described above, the neural networkcould be trained using crash data from approximately 25 crash andnon-crash events. In addition, the techniques of velocity and crashscaling were used to create a large library of crashes representing manyevents not staged by the automobile manufacturer. The resulting library,it is believed, represents the vast majority of crash events that occurin real world accidents for the majority of automobiles. Thus, theneural network algorithm comes close to the goal of a universalelectronic sensor usable on most if not all automobiles as furtherdescribed in U.S. Pat. No. 5,684,701. The results of this algorithm asreported in the '701 patent for a matrix of crashes created by thevelocity and crash scaling technique appears in FIGS. 7 and 8 of thatpatent.

The '701 patent describes the dramatic improvement achievable throughthe use of pattern recognition techniques for determining whether theairbag should be deployed. Such a determination is really a forecastingthat the eventual velocity change of the vehicle will be above anamount, such as about 12 mph, which requires airbag deployment. Theinstant invention extends this concept to indirectly predict what theeventual velocity change will in fact be when the occupant, representedby an unrestrained mass, impacts the airbag. Furthermore, it does so notjust at the time that the deployment decision is required but also, inthe preferred implementation, at all later times until adding orremoving additional gas from the airbag will have no significant injuryreducing effect. The neural network can be trained to predict orextrapolate this velocity but even that is not entirely sufficient. Whatis needed is to determine the flow rate of gas into and/or out of theairbag to optimize injury reduction which depends not only on theprediction or extrapolation of the velocity change at a particular pointin time but must take into account the prediction that was made at anearlier point when the decision was made to inject a given amount of gasinto the airbag. Also, the timing of when the velocity change will occuris a necessary parameter since gas is usually not only flowing into butout of the airbag and both flows must be taken into account. It is thusunlikely that an algorithm, which will perform well in all real worldcrashes, can be mathematically derived.

The neural network solves the problem by considering all of theacceleration up to the current point in the crash and therefore knowshow much gas has been put into the airbag and how much has flowed out.It can be seen that even if this problem could be solved mathematicallyfor all crashes, the mathematical approach becomes hopeless as soon asthe occupant properties are added.

Once a pattern recognition computer system is implemented in a vehicle,the same system can be used for many other pattern recognition functionssuch as the airbag system diagnostic. Testing that the pattern of theairbag system during the diagnostic test on vehicle startup, asrepresented by the proper resistances appearing across the wires to thevarious system components, for example, is an easy task for a patternrecognition system. The system can thus do all of the functions of theconventional SDM, sensing and diagnostics, as well as many others.

2. Diagnostics

A smart airbag system is really part of a general vehicle diagnosticsystem and many of the components that make up the airbag system and therest of the vehicle diagnostic system can be shared. Therefore, we willnow briefly discuss a general vehicle diagnostic system focusing on theinteraction with the occupant restraint system. This description istaken from U.S. Pat. No. 6,484,080.

For the purposes herein the following terms are defined as follows:

The term “component” refers to any part or assembly of parts that ismounted to or a part of a motor vehicle and which is capable of emittinga signal representative of its operating state that can be sensed by anyappropriate sensor. The following is a partial list of generalautomobile and truck components, the list not being exclusive:

Occupant restraints; engine; transmission; brakes and associated brakeassembly; tires; wheel; steering wheel and steering column assembly;water pump; alternator; shock absorber; wheel mounting assembly;radiator; battery; oil pump; fuel pump; air conditioner compressor;differential gear; exhaust system; fan belts; engine valves; steeringassembly; vehicle suspension including shock absorbers; vehicle wiringsystem; and engine cooling fan assembly.

The term “sensor” as used herein will generally refer to any measuring,detecting or sensing device mounted on a vehicle or any of itscomponents including new sensors mounted in conjunction with thediagnostic module in accordance with the invention. A partial,non-exhaustive list of common sensors mounted on an automobile or truckis:

airbag crash sensor; accelerometer; microphone; camera; antenna;capacitance sensor or other electromagnetic wave sensor; stress orstrain sensor; pressure sensor; weight sensor; magnetic field or fluxsensor; coolant thermometer; oil pressure sensor; oil level sensor; airflow meter; voltmeter; ammeter; humidity sensor; engine knock sensor;oil turbidity sensor; throttle position sensor; steering wheel torquesensor; wheel speed sensor; tachometer; speedometer; other velocitysensors; other position or displacement sensors; oxygen sensor; yaw,pitch and roll angular sensors; clock; odometer; power steering pressuresensor; pollution sensor; fuel gauge; cabin thermometer; transmissionfluid level sensor; gyroscopes or other angular rate sensors includingyaw, pitch and roll rate sensors; coolant level sensor; transmissionfluid turbidity sensor; break pressure sensor; tire pressure sensor;tire temperature sensor; tire acceleration sensor; GPS receiver; DGPSreceiver; coolant pressure sensor; occupant position sensor; andoccupant weight sensor.

The term “actuator” as used herein will generally refer to a device thatperforms some action upon receiving the proper signal. Examples ofactuators include:

window motor; door opening and closing motor; electric door lock; decklid lock; airbag inflator initiator; fuel injector; brake valves; pumps;relays; and steering assist devices.

The term “signal” as used herein will generally refer to any timevarying output from a component including electrical, acoustic, thermal,or electromagnetic radiation, or mechanical vibration.

Sensors on a vehicle are generally designed to measure particularparameters of particular vehicle components. However, frequently thesesensors also measure outputs from other vehicle components. For example,electronic airbag crash sensors currently in use contain anaccelerometer for determining the accelerations of the vehicle structureso that the associated electronic circuitry of the airbag crash sensorcan determine whether a vehicle is experiencing a crash of sufficientmagnitude so as to require deployment of the airbag.

An IMU using up to three accelerometers and up to three gyroscopes canalso be used. This accelerometer continuously monitors the vibrations inthe vehicle structure regardless of the source of these vibrations. If awheel is out-of-balance or delaminating, or if there is extensive wearof the parts of the front wheel mounting assembly, or wear in the shockabsorbers, the resulting abnormal vibrations or accelerations can, inmany cases, be sensed by the crash sensor accelerometer. There are othercases, however, where the sensitivity or location of the airbag crashsensor accelerometer is not appropriate and one or more additionalaccelerometers and/or gyroscopes or IMU may be mounted onto a vehiclefor the purposes of this invention. Some airbag crash sensors are notsufficiently sensitive accelerometers or have sufficient dynamic rangefor the purposes herein.

Every component of a vehicle emits various signals during its life.These signals can take the form of electromagnetic radiation, acousticradiation, thermal radiation, electric or magnetic field variations,vibrations transmitted through the vehicle structure, and voltage orcurrent fluctuations, depending on the particular component. When acomponent is functioning normally, it may not emit a perceptible signal.In that case, the normal signal is no signal, i.e., the absence of asignal. In most cases, a component will emit signals that change overits life and it is these changes that contain information as to thestate of the component, e.g., whether failure of the component isimpending. Usually components do not fail without warning. However, mostsuch warnings are either not perceived or if perceived are notunderstood by the vehicle operator until the component actually failsand, in some cases, a breakdown of the vehicle occurs. In a few years,it is expected that various roadways will have systems for automaticallyguiding vehicles operating thereon. Such systems have been called “smarthighways” and are part of the field of intelligent transportationsystems (ITS). If a vehicle operating on such a smart highway were tobreakdown, serious disruption of the system could result and the safetyof other users of the smart highway could be endangered.

Accelerometers are routinely used mounted outside of the crush zone forsensing the failure of the vehicle, that is, a crash of the vehicle.Looking at this in general terms, there is synergy between therequirements of sensing the status of the whole vehicle as well as itscomponents and the same sensors can often be used for multiple purposes.The output of a microphone mounted in the vehicle could be used to helpdetermine the existence and severity of a crash, for example.

In accordance with the invention, each of these signals emitted by thevehicle components is converted into electrical signals and thendigitized (i.e., the analog signal is converted into a digital signal)to create numerical time series data that is then entered into aprocessor. Pattern recognition algorithms are then applied in theprocessor to attempt to identify and classify patterns in this timeseries data. For a particular component, such as a tire for example, thealgorithm attempts to determine from the relevant digital data whetherthe tire is functioning properly and/or whether it requires balancing,additional air, or perhaps replacement. Future systems may bypass theA/D conversion and operate directly on the analog signals. Opticalcorrelation systems are now used by the military that create the Fouriertransform of an image directly using diffraction gratings and comparethe image with a stored image.

Frequently, the data entered into the computer needs to be pre-processedbefore being analyzed by a pattern recognition algorithm. The data froma wheel speed sensor, for example, might be used as is for determiningwhether a particular tire is operating abnormally in the event it isunbalanced, whereas the integral of the wheel speed data over a longtime period (integration being a pre-processing step), when compared tosuch sensors on different wheels, might be more useful in determiningwhether a particular tire is going flat and therefore needs air.

In some cases, the frequencies present in a set of data are a betterpredictor of component failures than the data itself. For example, whena motor begins to fail due to worn bearings, certain characteristicfrequencies began to appear. In most cases, the vibrations arising fromrotating components, such as the engine, will be normalized based on therotational frequency as disclosed in a recent NASA TSP. Moreover, theidentification of which component is causing vibrations present in thevehicle structure can frequently be accomplished through a frequencyanalysis of the data. For these cases, a Fourier transformation of thedata is made prior to entry of the data into a pattern recognitionalgorithm. Optical correlations systems using Fourier transforms canalso be applicable.

Other mathematical transformations are also made for particular patternrecognition purposes in practicing the teachings of this invention. Someof these include shifting and combining data to determine phase changesfor example, differentiating the data, filtering the data, and samplingthe data. Also, there exist certain more sophisticated mathematicaloperations that attempt to extract or highlight specific features of thedata. This invention contemplates the use of a variety of thesepreprocessing techniques, and combinations thereof, and the choice ofwhich one or ones is left to the skill of the practitioner designing aparticular diagnostic module.

Another technique that is contemplated for some implementations of thisinvention is the use of multiple accelerometers and/or microphones thatallow the system to locate the source of any measured vibrations basedon the time of flight, or time of arrival of a signal at differentlocations, and/or triangulation techniques. Once a distributedaccelerometer installation has been implemented to permit this sourcelocation, the same sensors can be used for smarter crash sensing as itwill permit the determination of the location of the impact on thevehicle. Once the impact location is known, a highly tailored algorithmcan be used to accurately forecast the crash severity making use ofknowledge of the force vs. crush properties of the vehicle at the impactlocation.

When a vehicle component begins to change its operating behavior, it isnot always apparent from the particular sensors, if any, which aremonitoring that component. Output from any one of these sensors can benormal even though the component is failing. By analyzing the output ofa variety of sensors, however, the pending failure can be diagnosed. Forexample, the rate of temperature rise in the vehicle coolant, if it weremonitored, might appear normal unless it were known that the vehicle wasidling and not traveling down a highway at a high speed. Even the levelof coolant temperature which is in the normal range could in fact beabnormal in some situations signifying a failing coolant pump, forexample, but not detectable from the coolant thermometer alone.

Pending failure of some components is difficult to diagnose andsometimes the design of the component requires modification so that thediagnosis can be more readily made. A fan belt, for example, frequentlybegins failing by a cracking of the inner surface. The belt can bedesigned to provide a sonic or electrical signal when this crackingbegins in a variety of ways. Similarly, coolant hoses can be designedwith an intentional weak spot where failure will occur first in acontrolled manner that can also cause a whistle sound as a small amountof steam exits from the hose. This whistle sound can then be sensed by ageneral purpose microphone, for example.

In FIG. 3, a generalized component 250 emitting several signals that aretransmitted along a variety of paths, sensed by a variety of sensors andanalyzed by the diagnostic device in accordance with the invention isillustrated schematically. Component 250 is mounted to a vehicle andduring operation, it emits a variety of signals such as acoustic 251,electromagnetic radiation 252, thermal radiation 253, current andvoltage fluctuations in conductor 254 and mechanical vibrations 255.Various sensors are mounted in the vehicle to detect the signals emittedby the component 250. These include one or more vibration sensors(accelerometers) 259, 261 and/or gyroscopes also mounted to the vehicle,one or more acoustic sensors 256, 262, electromagnetic radiation sensor257, heat radiation sensor 258, and voltage or current sensor 260. Inaddition, various other sensors 263, 264 measure other parameters ofother components that in some manner provide information directly orindirectly on the operation of component 250.

All of the sensors illustrated on FIG. 3 can be connected to a data bus265. A diagnostic module 266, in accordance with the invention, can alsobe attached to the vehicle data bus 265 and receives the signalsgenerated by the various sensors. The sensors may however be wirelesslyconnected to the diagnostic module 266 and be integrated into a wirelesspower and communications system or a combination of wired and wirelessconnections.

As shown in FIG. 3, the diagnostic module 266 has access to the outputdata of each of the sensors that have potential information relative tothe component 250. This data appears as a series of numerical valueseach corresponding to a measured value at a specific point in time. Thecumulative data from a particular sensor is called a time series ofindividual data points. The diagnostic module 266 compares the patternsof data received from each sensor individually, or in combination withdata from other sensors, with patterns for which the diagnostic module266 has been trained to determine whether the component 250 isfunctioning normally or abnormally. Note that although a general vehiclecomponent diagnostic system is being described, the state of somevehicle components can provide information to the vehicle safety system.A tire failure, for example, can lead to a vehicle rollover.

Important to this invention is the manner in which the diagnostic module266 determines a normal pattern from an abnormal pattern and the mannerin which it decides what data to use from the vast amount of dataavailable. This is accomplished using pattern recognition technologiessuch as artificial neural networks and training. The theory of neuralnetworks including many examples can be found in books on the subject.The neural network pattern recognition technology is one of the mostdeveloped of pattern recognition technologies. The neural network willbe used here to illustrate one example of a pattern recognitiontechnology but it is emphasized that this invention is not limited toneural networks. Rather, the invention may apply any known patternrecognition technology including sensor fusion and various correlationtechnologies. A brief description of the neural network patternrecognition technology is set forth below.

Neural networks are constructed of processing elements known as neuronsthat are interconnected using information channels call interconnects.Each neuron can have multiple inputs but generally only one output. Eachoutput however is connected to all other neurons in the next layer.Neurons in the first layer operate collectively on the input data asdescribed in more detail below. Neural networks learn by extractingrelational information from the data and the desired output. Neuralnetworks have been applied to a wide variety of pattern recognitionproblems including automobile occupant sensing, speech recognition,optical character recognition, and handwriting analysis.

To train a neural network, data is provided in the form of one or moretime series that represents the condition to be diagnosed as well asnormal operation. As an example, the simple case of an out-of-balancetire will be used. Various sensors on the vehicle can be used to extractinformation from signals emitted by the tire such as an accelerometer, atorque sensor on the steering wheel, the pressure output of the powersteering system, a tire pressure monitor or tire temperature monitor.Other sensors that might not have an obvious relationship to anunbalanced tire are also included such as, for example, the vehiclespeed or wheel speed. Data is taken from a variety of vehicles where thetires were accurately balanced under a variety of operating conditionsalso for cases where varying amounts of unbalance was intentionallyintroduced. Once the data has been collected, some degree ofpreprocessing or feature extraction is usually performed to reduce thetotal amount of data fed to the neural network. In the case of theunbalanced tire, the time period between data points might be chosensuch that there are at least ten data points per revolution of thewheel. For some other application, the time period might be one minuteor one millisecond.

Once the data has been collected, it is processed by a neuralnetwork-generating program, for example, if a neural network patternrecognition system is to be used. Such programs are availablecommercially, e.g., from NeuralWare of Pittsburgh, Pa. The programproceeds in a trial and error manner until it successfully associatesthe various patterns representative of abnormal behavior, an unbalancedtire, with that condition. The resulting neural network can be tested todetermine if some of the input data from some of the sensors, forexample, can be eliminated. In this way, the engineer can determine whatsensor data is relevant to a particular diagnostic problem. The programthen generates an algorithm that is programmed onto a microprocessor,microcontroller, neural processor, or DSP (herein collectively referredto as a microprocessor or processor). Such a microprocessor appearsinside the diagnostic module 266 in FIG. 3.

Once trained, the neural network, as represented by the algorithm, willnow operationally recognize an unbalanced tire on a vehicle when thisevent occurs. At that time, when the tire is unbalanced, the diagnosticmodule 266 will output a signal indicative of the unbalanced tire, suchas a signal to be sent to an output device which provides a message tothe driver indicating that the tire should be now be balanced asdescribed in more detail below. The message to the driver is provided byan output device coupled to or incorporated within the module 266 andmay be, e.g., a light on the dashboard, a vocal tone or any otherrecognizable indication apparatus. Messages can also be transmitter toothers outside of the vehicle such as other vehicles or to a vehicledealer. In some cases, control of the vehicle may be taken over by avehicle system in response to a message. In some cases, the vehiclecomponent failure portends an oncoming accident and one or more parts ofthe restraint system can be deployed.

It is important to note that there may be many neural networks involvedin a total vehicle diagnostic system. These can be organized either inparallel, series, as an ensemble, cellular neural network or as amodular neural network system. In one implementation of a modular neuralnetwork, a primary neural network identifies that there is anabnormality and tries to identify the likely source. Once a choice hasbeen made as to the likely source of the abnormality, another of a groupof neural networks is called upon to determine the exact cause of theabnormality. In this manner, the neural networks are arranged in a treepattern with each neural network trained to perform a particular patternrecognition task.

Operation of a neural network is well understood by those skilled in theart. Neural networks are the most well-known of the pattern recognitiontechnologies based on training, although neural networks have onlyrecently received widespread attention and have been applied to onlyvery limited and specialized problems in motor vehicles. Othernon-training based pattern recognition technologies exist, such as fuzzylogic. However, the programming required to use fuzzy logic, where thepatterns must be determined by the programmer, render these systemsimpractical for general vehicle diagnostic problems such as describedherein. Therefore, preferably the pattern recognition systems that learnby training are used herein. On the other hand, the combination ofneural networks and fuzzy logic, such as in a Neural-Fuzzy system, areapplicable and can result in superior results.

The neural network is the first highly successful of what will be avariety of pattern recognition techniques based on training. There isnothing that suggests that it is the only or even the best technology.The characteristics of all of these technologies which render themapplicable to this general diagnostic problem include the use oftime-based input data and that they are trainable. In all cases, thepattern recognition technology learns from examples of datacharacteristic of normal and abnormal component operation.

A diagram of one example of a neural network used for diagnosing anunbalanced tire, for example, based on the teachings of this inventionis shown in FIG. 2 (discussed above). The process can be programmed toperiodically test for an unbalanced tire. Since this need be done onlyinfrequently, the same processor can be used for many such diagnosticproblems. When the particular diagnostic test is run, data from thepreviously determined relevant sensors is preprocessed and analyzed withthe neural network algorithm. For the unbalanced tire, using the datafrom an accelerometer for example, the digital acceleration values fromthe analog to digital converter in the accelerometer are entered intonodes 1 through n and the neural network algorithm compares the patternof values on nodes 1 through n with patterns for which it has beentrained as follows.

Each of the input nodes is connected to each of the second layer nodes,h-1, h-2, . . . , h-n, called the hidden layer, either electrically asin the case of a neural computer, or through mathematical functionscontaining multiplying coefficients called weights. At each hidden layernode, a summation occurs of the values from each of the input layernodes, which have been operated on by functions containing the weights,to create a node value. Similarly, the hidden layer nodes are in likemanner connected to the output layer node(s), which in this example isonly a single node O representing the decision to notify the driver ofthe unbalanced tire. During the training phase, an output node value of1, for example, is assigned to indicate that the driver should benotified and a value of 0 is assigned to not providing an indication tothe driver.

In the example above, twenty input nodes were used, five hidden layernodes and one output layer node. In this example, only one sensor wasconsidered and accelerations from only one direction were used. If otherdata from other sensors such as accelerations from the vertical orlateral directions were also used, then the number of input layer nodeswould increase. Again, the theory for determining the complexity of aneural network for a particular application has been the subject of manytechnical papers and will not be presented in detail here. Determiningthe requisite complexity for the example presented here can beaccomplished by those skilled in the art of neural network design. Foran example of the use of a neural network crash sensor algorithm, seeU.S. Pat. No. 5,684,701. Note that the inventors of this inventioncontemplate all combinations of the teachings of the '701 patent andthose disclosed herein.

It is also possible to apply modular neural networks in accordance withthe invention wherein several neural network are trained, each having aspecific function relating to the detection of the abnormality in theoperation of the component. The particular neural network(s) used, i.e.,those to which input is provided or from which output is used, can bedetermined based on the measurements by one or more of the sensors.

Briefly, the neural network described above defines a method, using apattern recognition system, of sensing an unbalanced tire anddetermining whether to notify the driver and comprises:

(a) obtaining an acceleration signal from an accelerometer mounted on avehicle;

(b) converting the acceleration signal into a digital time series;

(c) entering the digital time series data into the input nodes of theneural network;

(d) performing a mathematical operation on the data from each of theinput nodes and inputting the operated on data into a second series ofnodes wherein the operation performed on each of the input node dataprior to inputting the operated on value to a second series node isdifferent from (e.g. may employ a different weight) that operationperformed on some other input node data;

(e) combining the operated on data from all of the input nodes into eachsecond series node to form a value at each second series node;

(f) performing a mathematical operation on each of the values on thesecond series of nodes and inputting this operated on data into anoutput series of nodes wherein the operation performed on each of thesecond series node data prior to inputting the operated on value to anoutput series node is different from that operation performed on someother second series node data;

(g) combining the operated on data from all of the second series nodesinto each output series node to form a value at each output series node;and

(h) notifying a driver or taking some other action if the value on oneoutput series node is within a selected range signifying that a tirerequires balancing.

This method can be generalized to a method of predicting that acomponent of a vehicle will fail comprising:

(a) sensing a signal emitted from the component;

(b) converting the sensed signal into a digital time series;

(c) entering the digital time series data into a pattern recognitionalgorithm;

(d) executing the pattern recognition algorithm to determine if thereexists within the digital time series data a pattern characteristic ofabnormal operation of the component; and

(e) notifying a driver or taking some other action, including, in somecases, deployment of an occupant restraint system, if the abnormalpattern is recognized.

The particular neural network described above contains a single seriesof hidden layer nodes. In some network designs, more than one hiddenlayer is used, although only rarely will more than two such layersappear. There are of course many other variations of the neural networkarchitecture illustrated above which appear in the referencedliterature. For the purposes herein, therefore, “neural network” will bedefined as a system wherein the data to be processed is separated intodiscrete values which are then operated on and combined in at least atwo-stage process and where the operation performed on the data at eachstage is, in general, different for each discrete value and where theoperation performed is at least determined through a training process.

Implementation of neural networks can take on at least two forms, analgorithm programmed on a digital microprocessor, DSP or in a neuralcomputer. In this regard, it is noted that neural computer chips are nowbecoming available.

In the example above, only a single component failure was discussedusing only a single sensor since the data from the single sensorcontains a pattern which the neural network was trained to recognize aseither normal operation of the component or abnormal operation of thecomponent. The diagnostic module 266 contains preprocessing and neuralnetwork algorithms for a number of component failures. The neuralnetwork algorithms are generally relatively simple, requiring only a fewhundred lines of computer code. A single general neural network programcan be used for multiple pattern recognition cases by specifyingdifferent coefficients for various terms, one set for each application.Thus, adding different diagnostic checks has only a small affect on thecost of the system. Also, the system has available to it all of theinformation available on the data bus. During the training process, thepattern recognition program sorts out from the available vehicle data onthe data bus or from other sources, those patterns that predict failureof a particular component. Sometimes more than one data bus is used. Forexample, in some cases, there is a general data bus and one reserved forsafety systems. Any number of data buses can of course be monitored.

In FIG. 4, a schematic of a vehicle with several components and severalsensors in their approximate locations on a vehicle is shown along witha total vehicle diagnostic system in accordance with the inventionutilizing a diagnostic module in accordance with the invention. A flowdiagram of information passing from the various sensors shown on FIG. 4onto a vehicle data bus and thereby into the diagnostic device inaccordance with the invention is shown in FIG. 5 along with outputs to adisplay 278 for notifying the driver and/or to the vehicle cellularphone 279, or other communication device, for notifying the dealer,vehicle manufacturer or other entity concerned with the failure of acomponent in the vehicle including the vehicle itself such as occurs ina crash. If the vehicle is operating on a smart highway, for example,the pending component failure information may also be communicated to ahighway control system and/or to other vehicles in the vicinity so thatan orderly exiting of the vehicle from the smart highway can befacilitated. FIG. 5 also contains the names of the sensors shownnumbered on FIG. 4.

Sensor 1 is a crash sensor having an accelerometer (alternately one ormore dedicated accelerometers can be used), sensor 2 is represents oneor more microphones, sensor 3 is a coolant thermometer, sensor 4 is anoil pressure sensor, sensor 5 is an oil level sensor, sensor 6 is an airflow meter, sensor 7 is a voltmeter, sensor 8 is an ammeter, sensor 9 isa humidity sensor, sensor 10 is an engine knock sensor, sensor 11 is anoil turbidity sensor, sensor 12 is a throttle position sensor, sensor 13is a steering torque sensor, sensor 14 is a wheel speed sensor, sensor15 is a tachometer, sensor 16 is a speedometer, sensor 17 is an oxygensensor, sensor 18 is a pitch/roll sensor, sensor 19 is a clock, sensor20 is an odometer, sensor 21 is a power steering pressure sensor, sensor22 is a pollution sensor, sensor 23 is a fuel gauge, sensor 24 is acabin thermometer, sensor 25 is a transmission fluid level sensor,sensor 26 is a yaw sensor, sensor 27 is a coolant level sensor, sensor28 is a transmission fluid turbidity sensor, sensor 29 is brake pressuresensor and sensor 30 is a coolant pressure sensor. Other possiblesensors include a temperature transducer, a pressure transducer, aliquid level sensor, a flow meter, a position sensor, a velocity sensor,a RPM sensor, a chemical sensor and an angle sensor, angular rate sensoror gyroscope.

If a distributed group of acceleration sensors or accelerometers areused to permit a determination of the location of a vibration source,the same group can, in some cases, also be used to measure the pitch,yaw and/or roll of the vehicle eliminating the need for dedicatedangular rate sensors. In addition, such a suite of sensors can also beused to determine the location and severity of a vehicle crash andadditionally to determine that the vehicle is on the verge of rollingover. Thus, the same suite of accelerometers optimally performs avariety of functions including inertial navigation, crash sensing,vehicle diagnostics, roll over sensing etc.

Consider now some examples. The following is a partial list of potentialcomponent failures and the sensors from the list on FIG. 5 that mightprovide information to predict the failure of the component:

Vehicle crash 1, 2, 14, 16, 18, 26, 31, 32, 33 Vehicle Rollover 1, 2,14, 16, 18, 26, 31, 32, 33 Out of balance tires 1, 13, 14, 15, 20, 21Front end out of alignment 1, 13, 21, 26 Tune up required 1, 3, 10, 12,15, 17, 20, 22 Oil change needed 3, 4, 5, 11 Motor failure 1, 2, 3, 4,5, 6, 10, 12, 15, 17, 22 Low tire pressure 1, 13, 14, 15, 20, 21 Frontend looseness 1, 13, 16, 21, 26 Cooling system failure 3, 15, 24, 27, 30Alternator problems 1, 2, 7, 8, 15, 19, 20 Transmission problems 1, 3,12, 15, 16, 20, 25, 28 Differential problems 1, 12, 14 Brakes 1, 2, 14,18, 20, 26, 29 Catalytic converter and muffler 1, 2, 12, 15, 22 Ignition1, 2, 7, 8, 9, 10, 12, 17, 23 Tire wear 1, 13, 14, 15, 18, 20, 21, 26Fuel leakage 20, 23 Fan belt slippage 1, 2, 3, 7, 8, 12, 15, 19, 20Alternator deterioration 1, 2, 7, 8, 15, 19 Coolant pump failure 1, 2,3, 24, 27, 30 Coolant hose failure 1, 2, 3, 27, 30 Starter failure 1, 2,7, 8, 9, 12, 15 Dirty air filter 2, 3, 6, 11, 12, 17, 22

Several interesting facts can be deduced from a review of the abovelist. First, all of the failure modes listed can be at least partiallysensed by multiple sensors. In many cases, some of the sensors merelyadd information to aid in the interpretation of signals received fromother sensors. In today's automobile, there are few if any cases wheremultiple sensors are used to diagnose or predict a problem. In fact,there is virtually no failure prediction undertaken at all. Second, manyof the failure modes listed require information from more than onesensor. Third, information for many of the failure modes listed cannotbe obtained by observing one data point in time as is now done by mostvehicle sensors. Usually, an analysis of the variation in a parameter asa function of time is necessary. In fact, the association of data withtime to create a temporal pattern for use in diagnosing componentfailures in automobile is believed to be unique to this invention as isthe combination of several such temporal patterns. Fourth, the vibrationmeasuring capability of the airbag crash sensor, or other accelerometer,is useful for most of the cases discussed above yet, at the time of thisinvention, there was no such use of accelerometers except as non-crushzone mounted crash sensors. The airbag crash sensor is used only todetect crashes of the vehicle. Fifth, the second most-used sensor in theabove list, a microphone, does not currently appear on any automobilesyet sound is the signal most often used by vehicle operators andmechanics to diagnose vehicle problems. Another sensor that is listedabove which also did not currently appear on automobiles at the time ofthis invention is a pollution sensor. This is typically a chemicalsensor mounted in the exhaust system for detecting emissions from thevehicle. It is expected that this and other chemical sensors will beused more in the future.

In addition, from the foregoing depiction of different sensors whichreceive signals from a plurality of components, it is possible for asingle sensor to receive and output signals from a plurality ofcomponents which are then analyzed by the processor to determine if anyone of the components for which the received signals were obtained bythat sensor is operating in an abnormal state. Likewise, it is alsopossible to provide for a multiplicity of sensors each receiving adifferent signal related to a specific component which are then analyzedby the processor to determine if that component is operating in anabnormal state. Note that neural networks can simultaneously analyzedata from multiple sensors of the same type or different types.

The discussion above has centered on notifying the vehicle operator of apending problem with a vehicle component. Today, there is greatcompetition in the automobile marketplace and the manufacturers anddealers who are most responsive to customers are likely to benefit byincreased sales both from repeat purchasers and new customers. Thediagnostic module disclosed herein benefits the dealer by making himinstantly aware, through the cellular telephone system, or othercommunication link, coupled to the diagnostic module or system inaccordance with the invention, when a component is likely to fail.

As envisioned, on some automobiles, when the diagnostic module 266detects a potential failure, it not only notifies the driver through adisplay 278, but also automatically notifies the dealer through avehicle cellular phone 279. The dealer can thus contact the vehicleowner and schedule an appointment to undertake the necessary repair ateach party's mutual convenience. The customer is pleased since apotential vehicle breakdown has been avoided and the dealer is pleasedsince he is likely to perform the repair work. The vehicle manufactureralso benefits by early and accurate statistics on the failure rate ofvehicle components. This early warning system can reduce the cost of apotential recall for components having design defects. It could evenhave saved lives if such a system had been in place during the Firestonetire failure problem. The vehicle manufacturer will thus be guidedtoward producing higher quality vehicles thus improving hiscompetitiveness. Finally, experience with this system will actually leadto a reduction in the number of sensors on the vehicle since only thosesensors that are successful in predicting failures will be necessary.

For most cases, it is sufficient to notify a driver that a component isabout to fail through a warning display. In some critical cases, actionbeyond warning the driver may be required. If, for example, thediagnostic module detected that the alternator was beginning to fail, inaddition to warning the driver of this eventuality, the module couldsend a signal to another vehicle system to turn off all non-essentialdevices which use electricity thereby conserving electrical energy andmaximizing the time and distance that the vehicle can travel beforeexhausting the energy in the battery. Additionally, this system can becoupled to a system such as ONSTAR® or a vehicle route guidance system,and the driver can be guided to the nearest open repair facility or afacility of his or her choice.

The diagnostic module of this invention assumes that a vehicle data busexists which is used by all of the relevant sensors on the vehicle. Mostvehicles manufactured at the time of this invention did not have a databus although it was widely believed that most vehicles will have one inthe near future. A vehicle safety bus has been considered for severalvehicle models. Relevant signals can be transmitted to the diagnosticmodule through a variety of coupling systems other than through a databus and this invention is not limited to vehicles having a data bus. Forexample, the data can be sent wirelessly to the diagnostic module usingthe Bluetooth or WiFi specification. In some cases, even the sensors donot have to be wired and can obtain their power via RF from theinterrogator as is well known in the RFID (radio frequencyidentification) field. Alternately, an inductive or capacitive powertransfer system can be used.

As can be appreciated, the invention described herein brings several newimprovements to automobiles including, but not limited to, use ofpattern recognition technologies to diagnose potential vehicle componentfailures, use of trainable systems thereby eliminating the need ofcomplex and extensive programming, simultaneous use of multiple sensorsto monitor a particular component, use of a single sensor to monitor theoperation of many vehicle components, monitoring of vehicle componentswhich have no dedicated sensors, and notification to the driver andpossibly an outside entity of a potential component failure in time sothat the failure can be averted and vehicle breakdowns substantiallyeliminated. Additionally, improvements to the vehicle stability, crashavoidance, crash anticipation and occupant protection are available.

To implement a component diagnostic system for diagnosing the componentutilizing a plurality of sensors not directly associated with thecomponent, i.e., independent of the component, a series of tests areconducted. For each test, the signals received from the sensors areinput into a pattern recognition training algorithm with an indicationof whether the component is operating normally or abnormally (thecomponent being intentionally altered to provide for abnormaloperation). Data from the test is used to generate the patternrecognition algorithm, e.g., a neural network, so that in use, the datafrom the sensors is input into the algorithm and the algorithm providesan indication of abnormal or normal operation of the component. Also, toprovide a more versatile diagnostic module for use in conjunction withdiagnosing abnormal operation of multiple components, tests may beconducted in which each component is operated abnormally while the othercomponents are operating normally, as well as tests in which two or morecomponents are operating abnormally. In this manner, the diagnosticmodule may be able to determine based on one set of signals from thesensors during use that either a single component or multiple componentsare operating abnormally. Of course, crash tests are also run to permitcrash sensing.

Furthermore, the pattern recognition algorithm may be trained based onpatterns within the signals from the sensors. Thus, by means of a singlesensor, it would be possible to determine whether one or more componentsare operating abnormally. To obtain such a pattern recognitionalgorithm, tests are conducted using a single sensor, such as amicrophone, and causing abnormal operation of one or more components,each component operating abnormally while the other components operatenormally and multiple components operating abnormally. In this manner,in use, the pattern recognition algorithm may analyze a signal from asingle sensor and determine abnormal operation of one or morecomponents. In some cases, simulations can be used to analyticallygenerate the relevant data.

The invention is also particularly useful in light of the foreseeableimplementation of smart highways. Smart highways will result in vehiclestraveling down highways under partial or complete control of anautomatic system, i.e., not being controlled by the driver. The on-boarddiagnostic system will thus be able to determine failure of a componentprior to and/or upon failure thereof and inform the vehicle's guidancesystem to cause the vehicle to move out of the stream of traffic, i.e.,onto a shoulder of the highway, in a safe and orderly manner. Moreover,the diagnostic system may be controlled or programmed to preventmovement of the disabled vehicle back into the stream of traffic untilrepair of the component is satisfactorily completed.

In a method in accordance with this embodiment, the operation of thecomponent would be monitored and if abnormal operation of the componentis detected, e.g., by any of the methods and apparatus disclosed herein(although other component failure systems may of course be used in thisimplementation), the vehicle guidance system which controls the movementof the vehicle would be notified, e.g., via a signal from the diagnosticmodule to the guidance system, and the guidance system would beprogrammed to move the vehicle out of the stream of traffic, or off ofthe restricted roadway, possibly to a service station or dealer, uponreception of the particular signal from the diagnostic module. Theautomatic guidance systems for vehicles traveling on highways may be anyexisting system or system being developed, such as one based onsatellite positioning techniques or ground-based positioning techniques.Since the guidance system may be programmed to ascertain the vehicle'sposition on the highway, it can determine the vehicle's currentposition, the nearest location out of the stream of traffic, or off ofthe restricted roadway, such as an appropriate shoulder or exit to whichthe vehicle may be moved, and the path of movement of the vehicle fromthe current position to the location out of the stream of traffic, oroff of the restricted roadway. The vehicle may thus be moved along thispath under the control of the automatic guidance system. In thealternative, the path may be displayed to a driver and the driver canfollow the path, i.e., manually control the vehicle. The diagnosticmodule and/or guidance system may be designed to prevent re-entry of thevehicle into the stream of traffic, or off of the restricted roadway,until the abnormal operation of the component is satisfactorilyaddressed.

FIG. 6 is a flow chart of a method for directing a vehicle off of aroadway if a component is operating abnormally. The component'soperation is monitored at 40 and a determination is made at 42 whetherits operation is abnormal. If not, the operation of the component ismonitored further (at periodic intervals). If the operation of thecomponent is abnormal, the vehicle can be directed off the roadway at44. More particularly, this can be accomplished by generating a signalindicating the abnormal operation of the component at 46, directing thissignal to a guidance system in the vehicle at 48 that guides movement ofthe vehicle off of the roadway at 50. Also, if the component isoperating abnormally, the current position of the vehicle and thelocation of a site off of the roadway can be determined at 52, e.g.,using satellite-based or ground-based location determining techniques, apath from the current location to the off-roadway location determined at54 and then the vehicle directed along this path at 56. Periodically, adetermination is made at 58 whether the component's abnormality has beensatisfactorily addressed and/or corrected and if so, the vehicle canre-enter the roadway and operation and monitoring of the component beginagain. If not, the re-entry of the vehicle onto the roadway is preventedat 60.

FIG. 7 schematically shows basic components for performing this method,i.e., a component operation monitoring system 62, an optionalsatellite-based or ground-based positioning system 64 and a vehicleguidance system 66.

FIG. 8 illustrates the placement of a variety of sensors, primarilyaccelerometers and/or gyroscopes, which can be used to diagnose thestate of the vehicle itself. Sensor 300 can measure the acceleration ofthe firewall or instrument panel and is located thereon generally midwaybetween the two sides of the vehicle. Sensor 301 can be located in theheadliner or attached to the vehicle roof above the side door.Typically, there will be two such sensors, one on either side of thevehicle. Sensor 302 is shown in a typical mounting location midwaybetween the sides of the vehicle attached to or near the vehicle roofabove the rear window. Sensor 305 is shown in a typical mountinglocation in the vehicle trunk adjacent the rear of the vehicle. One, twoor three such sensors can be used depending on the application. If threesuch sensors are used, one would be adjacent each side of vehicle andone in the center. Sensor 303 is shown in a typical mounting location inthe vehicle door and sensor 304 is shown in a typical mounting locationon the sill or floor below the door. Finally, sensor 306, which can bealso multiple sensors, is shown in a typical mounting location forwardin a forward crush zone of the vehicle. If three such sensors are used,one would be adjacent each vehicle side and one in the center.

In general, sensors 300-306 provide a measurement of the state of thesensor, such as its velocity, acceleration, angular orientation ortemperature, or a state of the location at which the sensor is mounted.Thus, measurements related to the state of the sensor 300-306 wouldinclude measurements of the acceleration of the sensor, measurements ofthe temperature of the mounting location as well as changes in the stateof the sensor and rates of changes of the state of the sensor. As such,any described use or function of the sensors 300-306 above is merelyexemplary and is not intended to limit the form of the sensor or itsfunction.

Each of the sensors 300-306 may be single axis, double axis or triaxialaccelerometers and/or gyroscopes typically of the MEMS type. MEMS standsfor microelectromechanical system and is a term known to those skilledin the art. These sensors 300-306 can either be wired to the centralcontrol module or processor directly wherein they would receive powerand transmit information, or they could be connected onto the vehiclebus or, in some cases, using RFID technology, the sensors can bewireless and would receive their power through RF from one or moreinterrogators located in the vehicle. RFID stands for radio frequencyidentification wherein sensors are each provided with an identificationcode and designed to be powered by the energy in a radio frequency wavecontaining that code which is emitted by the interrogator. In this case,the interrogators can be connected either to the vehicle bus or directlyto control module. Alternately, an inductive or capacitive power andinformation transfer system can be used.

One particular implementation will now be described. In this case, eachof the sensors 300-306 is a single or dual axis accelerometer. They aremade using silicon micromachined technology such as disclosed in U.S.Pat. No. 5,121,180 and U.S. Pat. No. 5,894,090. These are onlyrepresentative patents of these devices and there exist more than 100other relevant U.S. patents describing this technology. Commerciallyavailable MEMS gyroscopes such as from Systron Doner have accuracies ofapproximately one degree per second. In contrast, optical gyroscopestypically have accuracies of approximately one degree per hour.Unfortunately, the optical gyroscopes are prohibitively expensive forautomotive applications. On the other hand, typical MEMS gyroscopes arenot sufficiently accurate for many control applications.

Referring now to FIG. 9, one solution is to use an IMU 311 that cancontain up to three accelerometers and three gyroscopes all produced asMEMS devices. If the devices are assembled into a single unit andcarefully calibrated to remove all predictable errors, and then coupledwith a GPS 312 and/or DGPS system 314 using a Kalman filter embodied ina processor or other control unit 313, the IMU 311 can be made to haveaccuracies comparable with military grade IMU containing precisionaccelerometers and fiber optic gyroscopes at a small fraction of thecost of the military IMU.

Thus, in connection with the control of parts of the vehicle, locationinformation may be obtained from the GPS receiver 312 and input to apattern recognition system for consideration when determining a controlsignal for the part of the vehicle. Position information from the IMU311 could alternatively or additionally be provided to the patternrecognition system. The location determination by the GPS receiver 312and IMU 311 may be improved using the Kalman filter embodied inprocessor 313 in conjunction with the pattern recognition system todiagnose, for example, the state of the vehicle.

Another way to use the IMU 311, GPS receiver 312 and Kalman filterembodied in processor 313 would be to use the GPS receiver 312 andKalman filter in processor 313 to periodically calibrate the location ofthe vehicle as determined by the IMU 311 using data from the GPSreceiver 312 and the Kalman filter embodied in processor 313. A DGPSreceiver 314 could also be coupled to the processor 313 in which case,the processor 313 would receive information from the DGPS receiver 314and correct the determination of the location of the vehicle asdetermined by the GPS receiver 312 or the IMU 311.

The angular rate function can be obtained through placing accelerometersat two separated, non-co-located points in a vehicle and using thedifferential acceleration to obtain an indication of angular motion andangular acceleration. From the variety of accelerometers shown on FIG.8, it can be readily appreciated that not only will all accelerations ofkey parts of the vehicle be determined, but the pitch, yaw and rollangular rates can also be determined based on the accuracy of theaccelerometers. By this method, low cost systems can be developed which,although not as accurate as the optical gyroscopes, are considerablymore accurate than conventional MEMS gyroscopes. The pitch, yaw and rollof a vehicle can also be accurately determined using GPS and threeantennas by comparing the phase of the carrier frequency from asatellite.

Instead of using two accelerometers at separate locations on thevehicle, a single conformal MEMS-IDT gyroscope may be used. A MEMS-IDTgyroscope is a microelectromechanical system-interdigital transducergyroscope. Such a conformal MEMS-IDT gyroscope is described in a paperby V. K. Varadan, Conformal MEMS-IDT Gyroscopes and Their ComparisonWith Fiber Optic Gyro, incorporated in its entirety herein. The MEMS-IDTgyroscope is based on the principle of surface acoustic wave (SAW)standing waves on a piezoelectric substrate. A surface acoustic waveresonator is used to create standing waves inside a cavity and theparticles at the anti-nodes of the standing waves experience largeamplitude of vibrations, which serves as the reference vibrating motionfor the gyroscope. Arrays of metallic dots are positioned at theanti-node locations so that the effect of Coriolis force due to rotationwill acoustically amplify the magnitude of the waves. Unlike other MEMSgyroscopes, the MEMS-IDT gyroscope has a planar configuration with nosuspended resonating mechanical structures.

The system of FIG. 8 preferably uses dual axis accelerometers, andtherefore provides a complete diagnostic system of the vehicle itselfand its dynamic motion. Such a system is believed to be far moreaccurate than any system currently available in the automotive market.This system provides very accurate crash discrimination since the exactlocation of the crash can be determined and, coupled with knowledge ofthe force deflection characteristics of the vehicle at the accidentimpact site, an accurate determination of the crash severity and thusthe need for occupant restraint deployment can be made. Similarly, thetendency of a vehicle to roll-over can be predicted in advance andsignals sent to the vehicle steering, braking and throttle systems toattempt to ameliorate the rollover situation or prevent it. In the eventthat it cannot be prevented, the deployment side curtain airbags can beinitiated in a timely manner.

Similarly, the tendency of the vehicle to slide or skid can beconsiderably more accurately determined and again the steering, brakingand throttle systems commanded to minimize the unstable vehiclebehavior.

Thus, through the sample deployment of inexpensive accelerometers at avariety of locations in the vehicle, significant improvements are manyin the areas of vehicle stability control, crash sensing, rolloversensing, and resulting occupant protection technologies.

Finally, the combination of the outputs from these accelerometer sensorsand the output of strain gage weight sensors in a vehicle seat, or in/ona support structure of the seat, can be used to make an accurateassessment of the occupancy of the seat and differentiate betweenanimate and inanimate occupants as well as determining where in the seatthe occupants are sitting. This can be done by observing theacceleration signals from the sensors of FIG. 8 and simultaneously thedynamic strain gage measurements from seat mounted strain gages. Theaccelerometers provide the input function to the seat and the straingages measure the reaction of the occupying item to the vehicleacceleration and thereby provide a method for dynamically determiningthe mass of the occupying item and its location. This is particularlyimportant for occupant position sensing during a crash event. Bycombining the outputs of accelerometers and strain gages andappropriately processing the same, the mass and weight of an objectoccupying the seat can be determined as well as the gross motion of suchan object so that an assessment can be made as to whether the object isa life form such as a human being.

For this embodiment, sensor 307 in FIG. 8 (not shown) represents one ormore strain gage or bladder weight sensors mounted on the seat or inconnection with the seat or its support structure. Suitable mountinglocations and forms of weight sensors are discussed in U.S. Pat. No.6,242,701 and U.S. Pat. No. 6,442,504 and contemplated for use in thisinvention as well. The mass or weight of the occupying item of the seatcan thus be measured based on the dynamic measurement of the straingages with optional consideration of the measurements of accelerometerson the vehicle, which are represented by any of sensors 300-307.

3. Summary

This application is one in a series of applications covering safety andother systems for vehicles and other uses. The disclosure herein goesbeyond that needed to support the claims of the particular inventionthat is claimed herein. This is not to be construed that the inventorsare thereby releasing the unclaimed disclosure and subject matter intothe public domain. Rather, it is intended that patent applications havebeen or will be filed to cover all of the subject matter disclosedabove.

The inventions described above are, of course, susceptible to manyvariations, modifications and changes, all of which are within the skillof the art. It should be understood that all such variations,modifications and changes are within the spirit and scope of theinventions and of the appended claims. Similarly, it will be understoodthat applicant intends to cover and claim all changes, modifications andvariations of the examples of the preferred embodiments of the inventionherein disclosed for the purpose of illustration which do not constitutedepartures from the spirit and scope of the present invention asclaimed.

Although several preferred embodiments are illustrated and describedabove, there are possible combinations using other geometries, materialsand different dimensions for the components and different forms of theneural network implementation that perform the same functions. Also, theneural network has been described as an example of one patternrecognition system. Other pattern recognition systems exist and stillothers are under development and will be available in the future. Such asystem can be used to identify crashes requiring the deployment of anoccupant restraint system and then, optionally coupled with additionalinformation related to the occupant, for example, create a system thatsatisfies the requirements of one of the Smart Airbag Phases. Also, withthe neural network system described above, the input data to the networkmay be data which has been pre-processed rather than the rawacceleration data either through a process called “feature extraction”,as described in Green (U.S. Pat. No. 4,906,940) for example, or byintegrating the data and inputting the velocity data to the system, forexample. This invention is not limited to the above embodiments andshould be determined by the following claims.

1. A method for controlling weighing of a vehicle travelling on a roadby means of a weigh station alongside the road, comprising: determiningweight of the vehicle using a processor on-board the vehicle; andtransmitting from the vehicle using a telematics device, the determinedweight of the vehicle to the weigh station.
 2. The method of claim 1,wherein the step of determining the weight of the vehicle comprisesprocessing inertial property data from an inertial measurement unit(IMU) into an indication of the weight of the vehicle.
 3. The method ofclaim 2, wherein the inertial property data from the IMU is multiplesets of inertial property data obtained over time.
 4. The method ofclaim 2, further comprising calibrating the IMU, using the processor,based differential motion of the vehicle over a period of time asdetermined by a location positioning system.
 5. The method of claim 4,wherein the IMU calibrating step comprises using a Kalman filter.
 6. Themethod of claim 1, wherein a vehicle transmitting its determined weightavoids stopping at the weigh station.
 7. A method for controlling travelof vehicles on a road having a weigh station alongside the road at whichvehicles must provide their vehicle weight at least for some designatedtimes and designated vehicle types, comprising: for those vehiclesequipped with an on-board weight determining unit, determining weight ofthe vehicle using a processor on-board the vehicle; and transmittingfrom the vehicle using a telematics device, the determined weight of thevehicle to the weigh station, whereby a vehicle transmitting itsdetermined weight avoids stopping at the weigh station.
 8. The method ofclaim 7, wherein the on-board weight determining unit includes aninertial measurement unit (IMU) and the step of determining the weightof the vehicle comprises processing inertial property data from the IMUinto an indication of the weight of the vehicle.
 9. The method of claim8, wherein the inertial property data from the IMU is multiple sets ofinertial property data obtained over time.
 10. The method of claim 8,further comprising calibrating the IMU, using the processor, baseddifferential motion of the vehicle over a period of time as determinedby a location positioning system.
 11. The method of claim 10, whereinthe IMU calibrating step comprises using a Kalman filter.
 12. A methodfor managing road information, comprising: obtaining road propertiesfrom vehicles during travel on the vehicle on a road at an off-vehiclelocation, the road properties being obtained using an inertialmeasurement unit on board each vehicle and transmitted from the vehicleusing a telematics device; storing the road properties from the vehiclesat the off-vehicle locations in a data storage device; and selectivelydistributing from the data storage device to the telematics device onvehicles traveling on the road, at least part of the collected roadproperties to the vehicles traveling on the road.
 13. The method ofclaim 12, further comprising associating the road properties with theweather in the area of the road at the off-vehicle location, the atleast part of the collected road properties being distributed to thevehicles traveling on the road based on the weather.